1. In logic and reasoning:
* Deductive Inference: Drawing a logically certain conclusion from a set of premises. Example: All men are mortal. Socrates is a man. Therefore, Socrates is mortal.
* Inductive Inference: Drawing a probable conclusion based on evidence or observations. Example: The sun has risen every day in the past. Therefore, the sun will rise tomorrow. This conclusion is likely but not guaranteed.
* Abductive Inference: Explaining an observation by finding the simplest and most likely explanation. Example: You hear a loud bang. You infer that it was a car backfiring, as this is the most common explanation in your context.
2. In statistics and machine learning:
* Statistical Inference: Using data to draw conclusions about a population. This often involves hypothesis testing, confidence intervals, and other statistical techniques.
* Machine Learning Inference: Using a trained model to make predictions or decisions on new data. For example, a machine learning model trained on images of cats and dogs could be used to infer whether a new image contains a cat or a dog.
3. In general usage:
* Drawing conclusions: When we infer something, we are using our knowledge and understanding to make a judgment about something that is not directly observed. This can be based on experience, logic, or intuition.
In summary: Inference involves making conclusions based on available information, using logic, evidence, or experience. The type of inference used will depend on the context and the desired level of certainty.